andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-492 knowledge-graph by maker-knowledge-mining
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Introduction: Since this blog in November, I’ve given my talk on infovis vs. statistical graphics about five times: once in person (at the visualization meetup in NYC, a blog away from Num Pang!) and the rest via telephone conferencing or skype. The live presentation was best, but the remote talks have been improving, and I’m looking forward to doing more of these in the future to save time and reduce pollution. Here are the powerpoints of the talk. Now that I’ve got it working well (mostly by cutting lots of words on the slides), my next step will be to improve the interactive experience. At the very least, I need to allocate time after the talk for discussion. People usually don’t ask a lot of questions when I speak, so maybe the best strategy is to allow a half hour following the talk for people to speak with me individually. It could be set up so that I’m talking with one person but the others who are hanging out could hear the conversation too. Anyway, one of the times I gave th
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1 Since this blog in November, I’ve given my talk on infovis vs. [sent-1, score-0.389]
2 statistical graphics about five times: once in person (at the visualization meetup in NYC, a blog away from Num Pang! [sent-2, score-0.409]
3 The live presentation was best, but the remote talks have been improving, and I’m looking forward to doing more of these in the future to save time and reduce pollution. [sent-4, score-0.226]
4 Now that I’ve got it working well (mostly by cutting lots of words on the slides), my next step will be to improve the interactive experience. [sent-6, score-0.082]
5 At the very least, I need to allocate time after the talk for discussion. [sent-7, score-0.273]
6 People usually don’t ask a lot of questions when I speak, so maybe the best strategy is to allow a half hour following the talk for people to speak with me individually. [sent-8, score-0.373]
7 It could be set up so that I’m talking with one person but the others who are hanging out could hear the conversation too. [sent-9, score-0.174]
8 Anyway, one of the times I gave the talk, a new idea came out: One thing that people like about infovis is the puzzle-solving aspect. [sent-10, score-0.441]
9 For example, when someone sees that horrible map with the plane crashes (see page 23 of the presentation ), there is a mini-joy of discovery at noticing–Hey, that’s Russia! [sent-11, score-0.4]
10 From our perspective as statisticians, it’s a cheap thrill: the reader is wasting brainpower to discover the obvious. [sent-14, score-0.346]
11 In this way, an attractive data visualization is functioning like a Chris Rock routine, when he says something that we all know, but he says it in such a fresh new way that we find it appealing. [sent-16, score-0.492]
12 Conversely, in statistical graphics we use a boring display so that anything unexpected will stand out. [sent-17, score-0.108]
13 I’m not saying that statisticians are better than infovis people, just that we strive for different effects. [sent-19, score-0.426]
14 Another example are those maps that distort the sizes of states or countries to be proportional to population. [sent-20, score-0.573]
15 Everybody loves these “cartograms,” but I hate ‘em. [sent-21, score-0.092]
16 Because the #1 thing these maps convey is that some states on the east coast have high population density and that nobody lives in Wyoming. [sent-23, score-0.645]
17 People loooove to see these wacky maps and discover these facts. [sent-24, score-0.686]
18 It’s like being on vacation in some far-off place and running into Aunt Louise at the grocery store. [sent-25, score-0.202]
19 In particular, I like the New York Times maps that show congressional and electoral college results within stylized states that include congressional districts as little squares. [sent-28, score-0.933]
20 These maps do the job by focusing attention on the results, not on the cool processes used to create the distortions. [sent-29, score-0.34]
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Introduction: Since this blog in November, I’ve given my talk on infovis vs. statistical graphics about five times: once in person (at the visualization meetup in NYC, a blog away from Num Pang!) and the rest via telephone conferencing or skype. The live presentation was best, but the remote talks have been improving, and I’m looking forward to doing more of these in the future to save time and reduce pollution. Here are the powerpoints of the talk. Now that I’ve got it working well (mostly by cutting lots of words on the slides), my next step will be to improve the interactive experience. At the very least, I need to allocate time after the talk for discussion. People usually don’t ask a lot of questions when I speak, so maybe the best strategy is to allow a half hour following the talk for people to speak with me individually. It could be set up so that I’m talking with one person but the others who are hanging out could hear the conversation too. Anyway, one of the times I gave th
Introduction: I continue to struggle to convey my thoughts on statistical graphics so I’ll try another approach, this time giving my own story. For newcomers to this discussion: the background is that Antony Unwin and I wrote an article on the different goals embodied in information visualization and statistical graphics, but I have difficulty communicating on this point with the infovis people. Maybe if I tell my own story, and then they tell their stories, this will point a way forward to a more constructive discussion. So here goes. I majored in physics in college and I worked in a couple of research labs during the summer. Physicists graph everything. I did most of my plotting on graph paper–this continued through my second year of grad school–and became expert at putting points at 1/5, 2/5, 3/5, and 4/5 between the x and y grid lines. In grad school in statistics, I continued my physics habits and graphed everything I could. I did notice, though, that the faculty and the other
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Introduction: To continue our discussion from last week , consider three positions regarding the display of information: (a) The traditional tabular approach. This is how most statisticians, econometricians, political scientists, sociologists, etc., seem to operate. They understand the appeal of a pretty graph, and they’re willing to plot some data as part of an exploratory data analysis, but they see their serious research as leading to numerical estimates, p-values, tables of numbers. These people might use a graph to illustrate their points but they don’t see them as necessary in their research. (b) Statistical graphics as performed by Howard Wainer, Bill Cleveland, Dianne Cook, etc. They–we–see graphics as central to the process of statistical modeling and data analysis and are interested in graphs (static and dynamic) that display every data point as transparently as possible. (c) Information visualization or infographics, as performed by graphics designers and statisticians who are
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Introduction: This post is by Phil. Michael Graham Richard has posted some great maps from the 1932 Atlas of the Historical Geography of the United States; the maps show how long it took to get to various places in the U.S. from New York City in 1800, 1830, 1857, and 1930. (I wonder if the atlas has one from around 1900 as well, that didn’t make it into the article? I’d like to see it, too, if it exists.) Worth a look. This post is by Phil. Time to get to anywhere in the conterminous states from New York City, in 1857
Introduction: Our discussion on data visualization continues. One one side are three statisticians–Antony Unwin, Kaiser Fung, and myself. We have been writing about the different goals served by information visualization and statistical graphics. On the other side are graphics experts (sorry for the imprecision, I don’t know exactly what these people do in their day jobs or how they are trained, and I don’t want to mislabel them) such as Robert Kosara and Jen Lowe , who seem a bit annoyed at how my colleagues and myself seem to follow the Tufte strategy of criticizing what we don’t understand. And on the third side are many (most?) academic statisticians, econometricians, etc., who don’t understand or respect graphs and seem to think of visualization as a toy that is unrelated to serious science or statistics. I’m not so interested in the third group right now–I tried to communicate with them in my big articles from 2003 and 2004 )–but I am concerned that our dialogue with the graphic
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Introduction: Yair points us to this page full of wonderful graphs from the Stephen Wolfram blog. Here are a few: And some words: People talk less about video games as they get older, and more about politics and the weather. Men typically talk more about sports and technology than women—and, somewhat surprisingly to me, they also talk more about movies, television and music. Women talk more about pets+animals, family+friends, relationships—and, at least after they reach child-bearing years, health. . . . Some of this is rather depressingly stereotypical. And most of it isn’t terribly surprising to anyone who’s known a reasonable diversity of people of different ages. But what to me is remarkable is how we can see everything laid out in such quantitative detail in the pictures above—kind of a signature of people’s thinking as they go through life. Of course, the pictures above are all based on aggregate data, carefully anonymized. But if we start looking at individuals, we’ll s
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Introduction: Eytan Adar writes: I was just going through the latest draft of your paper with Anthony Unwin . I heard part of it at the talk you gave (remotely) here at UMich. I’m curious about your discussion of the Baby Name Voyager . The tool in itself is simple, attractive, and useful. No argument from me there. It’s an awesome demonstration of how subtle interactions can be very helpful (click and it zooms, type and it filters… falls perfectly into the Shneiderman visualization mantra). It satisfies a very common use case: finding appropriate names for children. That said, I can’t help but feeling that what you are really excited about is the very static analysis on last letters (you spend most of your time on this). This analysis, incidentally, is not possible to infer from the interactive application (which doesn’t support this type of filtering and pivoting). In a sense, the two visualizations don’t have anything to do with each other (other than a shared context/dataset).
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Introduction: I stumbled across a chart that’s in my opinion the best way to express a comparison of quantities through time: It compares the new PC companies, such as Apple, to traditional PC companies like IBM and Compaq, but on the same scale. If you’d like to see how iPads and other novelties compare, see here . I’ve tried to use the same type of visualization in my old work on legal data visualization . It comes from a new market research firm Asymco that also produced a very clean income vs expenses visualization (click to enlarge): While the first figure is pure perfection, Tufte purists might find the second one too colorful. But to a busy person, color helps tell things apart: when I know that pink means interest, it takes a fraction of the second to assess the situation. We live in 2012, not in 1712 to have to think black and white. Finally, they have a few other interesting uses of interactive visualization, such as cellular-broadband infrastructure around
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Introduction: Paul Pudaite writes in response to my discussion with Bartels regarding effect sizes and measurement error models: You [Gelman] wrote: “I actually think there will be some (non-Gaussian) models for which, as y gets larger, E(x|y) can actually go back toward zero.” I [Pudaite] encountered this phenomenon some time in the ’90s. See this graph which shows the conditional expectation of X given Z, when Z = X + Y and the probability density functions of X and Y are, respectively, exp(-x^2) and 1/(y^2+1) (times appropriate constants). As the magnitude of Z increases, E[X|Z] shrinks to zero. I wasn’t sure it was worth the effort to try to publish a two paragraph paper. I suspect that this is true whenever the tail of one distribution is ‘sufficiently heavy’ with respect to the tail of the other. Hmm, I suppose there might be enough substance in a paper that attempted to characterize this outcome for, say, unimodal symmetric distributions. Maybe someone can do this? I think i
Introduction: She was assigned to teach a class in “evidence-based medicine”! ( link from my usual news source). I wonder what was in the syllabus? If anyone has a copy, feel free to send to me and I will post it here. My favorite part of the story, though, is this: Almost all physician assistant students refused to comment to a reporter Tuesday, saying they’d been told by the department not to talk to media. Talk about obedience to authority! They’re studying in a program that offers nonexistent courses, but then they follow the department’s gag order.
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